The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Genetic programming in classifying large-scale data: an ensemble method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Email Granulation Based On Augmented Interval Type-2 Fuzzy Set Methodologies
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
A rough margin based support vector machine
Information Sciences: an International Journal
On-line fuzzy modeling via clustering and support vector machines
Information Sciences: an International Journal
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
IEEE Transactions on Knowledge and Data Engineering
FRSVMs: Fuzzy rough set based support vector machines
Fuzzy Sets and Systems
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Fuzzy Support Vector Machine for bankruptcy prediction
Applied Soft Computing
Use of a fuzzy granulation--degranulation criterion for assessing cluster validity
Fuzzy Sets and Systems
Rough-wavelet granular space and classification of multispectral remote sensing image
Applied Soft Computing
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Support vector machine classification based on fuzzy clustering for large data sets
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Preprocessing unbalanced data using support vector machine
Decision Support Systems
IEEE Transactions on Neural Networks
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
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With the widespread application of computer and communication technologies, more and more real-time systems are implemented whose large amounts of time-stamped data consequently require more efficient processing approaches. For large-scale time series, precise values are often hard or even impossible to predict in limited time at limited costs. Meanwhile, precision is not absolutely necessary for human to think and reason, so credible changing ranges of time series are satisfactory for some decision-making problems. This study aims to develop fast interval predictors for large-scale, nonlinear time series with noisy data using fuzzy granular support vector machines (FGSVMs). Six information granulation methods are proposed which can granulate large-scale time series into subseries. FGSVM predictors are developed to forecast credible changing ranges of large-scale time series. Five performance indicators are presented to measure the quality and efficiency of FGSVMs. Four time series are used to examine the effectiveness and efficiency of the proposed granulation methods and the developed FGSVMs, whose results show the effectiveness and advantages of FGSVMs for large-scale, nonlinear time series with noisy data.